The objectives of this work are
(1) to define utilization-based class encodings and AI model fingerprints,
(2) to measure class encodings in architectures beyond small models (e.g., LeNet model with 60K parameters) and toy datasets, such as MNIST (Modified NIST dataset with 70K images of size \(28 \times 28\)  pixels), and
(3) to identify encoding patterns (motifs) that discriminate AI models without and with hidden classes (denoted as clean and poisoned AI models).
Our ultimate objective is to identify and decompose AI model computation graphs into subgraphs (subnetworks) that serve specific detection, segmentation, classification, or recognition purposes.
Building a library of subgraphs with semantic interpretations, such as a subgraph for wheel detection, creates opportunities to custom-design AI architectures for specific training datasets and avoid exploring a huge search space of graphs as done in the work of Ying et al. [1]}.
This objective is aligned with the search for visual patterns in a collection of visualizations of AI layers and neurons under the OpenAI Microscope project [2]}.
By understanding class encoding patterns, one can additionally benefit from reduced AI model storage and inference computational requirements via more efficient network architecture search [1]} with advanced hardware [4]}. Furthermore, one can improve expressiveness of AI model architectures via design [5]} and efficiency measurements [6]} or one can assist in diagnosing failure modes [7]}.
